Model-Based Selection Of Trade Execution Strategies

ABSTRACT

Effective selection of trade execution strategies using a multi-dimensional model is disclosed. A relationship exists between order difficulty and execution strategy. Execution strategy depends on order difficulty, and order difficulty has many dimensions. The multi-dimensional model classifies trade orders according to the dimensions, and then maps these classified trade orders into suitable execution strategies. For each trade order, one or more appropriate strategies are automatically selected and presented to the trader to assist the trader in making an informed and timely decision.

TECHNICAL FIELD

This disclosure relates to trading, and particularly to selection oftrade execution strategies.

BACKGROUND

Financial markets depend on efficient execution of trades to buy andsell equities. Historically, human traders executed the trade with onetrader representing the seller (the “sell-side trader”) and a secondtrader representing the buyer (the “buy-side trader”). As trading volumeincreased, more trading was performed automatically by computerizedsystems, such as the NASDAQ marketplace and ECNs (ElectronicCommunications Networks). The types of trades have also grown morecomplex. Today, there are many different types of trades of varyinglevels of complexity, and equally many diverse trading strategies toexecute these trades.

One of the challenges encountered by a buy-side trader is how to choosethe right execution strategy from a large number of possible executionstrategies. Consider a simple order to buy 2,000 shares of fictitiouslarge-cap XYZ Corp. The trade order is presented on the trader's screenand the trader is immediately faced with many decisions. Should thistrade be executed over 30 minutes or 3 days? Should the trader usemarket orders or limit orders? Does the trader execute the trade as partof a portfolio trade or as a single stock trade? Should the trader use atraditional broker-dealer or direct market access (DMA)? The trader mustmake many snap decisions, select an execution strategy based on thesedecisions, and then allow the system to complete the trade.

Efficient trade execution thereby requires timely selection andapplication of the most suitable execution strategy for a particulartrade. This is not so simple. Indeed, the problem of choosing an optimumexecution strategy gets very complicated, very quickly. In a rapidtrading environment, the trader commonly sees a large volume of tradesflash across the screen. The trader is forced to make executiondecisions in a very short timeframe. Further, each execution decisionhas a real cost associated with it. For instance, if the trade order isdifficult, there is a preference for a “high touch” strategy thatinvolves a human trader. The human trader charges a higher premium forexecuting the trade. Conversely, if the trade order is not overlycomplex, a “low touch” strategy that calls for computerized execution ofthe trade might be preferred. The cost to execute a trade electronicallyis generally significantly lower (sometimes orders of magnitude lower)than that involving the human trader.

Accordingly, there is a need for improved techniques to assist tradersin making timely decisions to identify appropriate execution strategiesthat minimize the costs of executing the trade.

SUMMARY

Effective selection of trade execution strategies using amulti-dimensional model is disclosed. A relationship exists betweenorder difficulty and execution strategy. Execution strategy depends onorder difficulty, and order difficulty has many dimensions. Themulti-dimensional model classifies trade orders according to thedimensions, and then maps these classified trade orders into suitableexecution strategies. For each trade order, one or more appropriatestrategies are automatically selected and presented to the trader toassist the trader in making an informed and timely decision.

BRIEF DESCRIPTION OF THE CONTENTS

The detailed description is described with reference to the accompanyingfigures. In the figures, the left-most digit(s) of a reference numberidentifies the figure in which the reference number first appears. Theuse of the same reference numbers in different figures indicates similaror identical items.

FIG. 1 illustrates an exemplary trading architecture in which multiplebuyers exchange financial instruments with multiple sellers as part of amarketplace. Within this architecture, buy-side traders employ amodel-based selection tool for selecting trade execution strategies.

FIG. 2 illustrates one implementation of a multi-dimensional model,which is graphically represented as a three-dimensional cube.

FIG. 3 is a diagram showing one possible mapping of trade ordercategories defined by the cube of FIG. 2 into execution strategies.

FIG. 4 is a functional block diagram of a buy-side trading system thatruns a strategy selection tool that automates selection of tradeexecution strategies using the multi-dimensional model.

FIG. 5 is a flow diagram of a process for establishing and using amulti-dimensional model for selecting trade execution strategies.

FIG. 6 is a flow diagram of one example implementation for handlingtrade orders using the multi-dimensional model.

DETAILED DESCRIPTION

This disclosure is directed to computerized selection of executionstrategies for various types of trades. A framework is presented formapping trade orders into execution strategies according to orderdifficulty. Order difficulty has many factors or dimensions. Theframework can thus be conceptualized as a multi-dimensional model, whereeach dimension represents a different measure of order difficulty. Inits simplest form, the model can be represented as a cube with threedimensions of order difficulty. For equity trading, one possible set ofdimensions are liquidity, order size, and trade urgency.

Trade orders are classified within the model according to the threedimensions of order difficulty. Assuming the three dimensions ofliquidity, order size, and trade urgency, the easiest orders representedby the cube are low-urgency, small orders in large-cap stocks. Incontrast, the most difficult orders are high-urgency, large orders insmall-cap stocks. Once defined, the multi-dimensional model is mappedinto execution strategies that are suitable for executing the tradeorders. In this manner, trade orders are initially assessed andclassified by the model and then mapped to the appropriate executionstrategies. The execution strategies are presented to the trader,thereby providing a practical decision-making tool.

For discussion purposes, the framework will be described in the contextof a trading system used to trade equities in publicly traded companies.However, the framework may be used to identify trade executionstrategies for other types of financial instruments, such as bonds,currency, debt, derivatives, and the like.

Architecture

FIG. 1 illustrates an architecture 100 that represents an exemplarytrading environment in which multiple buyers 102(1), . . . , 102(M)exchange equities (or other financial instruments) with multiple sellers104(1), . . . , 104(N) as part of a marketplace. The buyers and sellersmay be individuals, corporations, or other trading entities. The buyers102 and sellers 104 use computerized systems to electronically placeorders to buy and sell equities. Server computers are illustrated, butother types of computerized systems may be employed. The buyer andseller computers are coupled to communicate over one or more networks106, which is representative of any number or combination of differentnetworks, including proprietary data networks, the Internet, wirelessnetworks, satellite networks, and the like.

Each buyer 102 implements a buy-side trading system 108, which isdiagrammatically represented as being implemented on a networked servercomputer, although other arrangements and configurations are possible.The trading system 108 supports a strategy selection tool 110 thatreceives trade orders placed by the sellers 104 and assists the buy-sidetrader in timely selecting suitable trade execution strategies forexecuting the trade orders.

Selection of execution strategies is based on a multi-dimensional model112, which contemplates multiple dimensions of order difficulty. Thestrategy selection tool 110 evaluates the trade orders according to themodel 112 and timely presents recommended execution strategies totraders to assist them in making optimal decisions in a relatively shorttimeframe.

For any given trade order, there may be one or many different executionstrategies. The strategy selection tool 110 maintains a library ofpossible execution strategies 114. Such strategies might include, forexample:

Single stock trading or portfolio trading

Broker-dealer for capital or agency executions

Direct Market Access (DMA) to Electronic Communication Networks (ECNs)

Direct Market Access (DMA) to Crossing networks

Traditional floor-based exchanges

Broker platforms (e.g., REDIPlus™ from Goldman Sachs & Co.)

Algorithmic trading

Once a strategy is identified and selected, the trading system 108directs execution of the trade orders according to the chosen executionstrategy. This may involve routing the orders to an appropriate entityto execute the trade. Example entities shown in FIG. 1 include ECNs 116and broker platforms 118, although there are many other types ofentities.

One example implementation of the trading system 108 and strategyselection tool 110 is described below in more detail with reference toFIG. 4. Prior to this description, however, is an explanation of oneexample multi-dimensional model used to select appropriate tradeexecution strategies.

Multi-Dimensional Model

The strategy selection tool 110 uses the multi-dimensional model 112 tomap trade orders into execution strategies. This mapping is based uponorder difficulty, which has many dimensions. The multi-dimensional model112 has at least three dimensions. Hence, in its simplest form, themodel 112 can be represented geometrically as a cube.

FIG. 2 shows the multi-dimensional model 112 graphically represented asa three-dimensional cube 200. Cube 200 represents three dimensions oforder difficulty that influence a choice of execution strategies. In oneimplementation, the three dimensions of order difficulty are stockliquidity, order size, and trade urgency. In other implementations,different sets of dimensions may be used. The three dimensionscorrespond to the three axes of the cube 200. Each dimension has one ormore quantifiable components that can be used to measure or otherwisequantify the corresponding dimension.

Stock liquidity is one dimension of order difficulty. Generally, tradeorders for stocks with more liquidity are easier to execute than ordersfor stocks with less liquidity. A stock liquidity dimension 202 isrepresented along an x-axis of the cube 200 and ranges from moreliquidity to less liquidity. The stock liquidity dimension 202 can bequantified by one or more quantifiable components that affect liquidity.One example liquidity component is market capitalization, where publiclytraded companies with a large market capitalization (“large-cap stocks”)stocks, tend to be more liquid than publicly traded companies with asmall market capitalization (“small-cap stocks”). Using marketcapitalization as a measure, for example, the stock liquidity dimension202 would be quantified according to a dollar amount reflected in themarket capitalization, scaling from large-cap stocks (e.g., $10 billionor more) to small-cap stocks (e.g., less than $1 billion). The executionstrategy for orders in large-cap stocks is different than for orders insmall-cap stocks.

Another quantifiable component that may be used to measure the liquiditydimension 202 is the market exchange in which the equity is traded.Stocks traded on larger exchanges (e.g., NYSE, NASDAQ, London, Tokyo,etc.) tend to be more liquid than, say, regional over-the-counter (OTCs)exchanges that handle smaller or “penny” stocks. Timing is yet anothercomponent of the liquidity dimension 202. For example, stocks tend to beless liquid just prior to quarterly earnings news and more liquidfollowing earning announcements. Irregular announcements or news itemsmay also be considered a component of the liquidity dimension, as stockstend to be more liquid following such events. These components areexamples, and the skilled artisan will appreciate that there may be manyother components that affect the liquidity dimension 202.

Order size is another dimension of order difficulty that is used in thecube 200. Generally, trade orders for larger numbers of shares are moredifficult to execute than orders for smaller numbers of shares (unless,perhaps, the number is very small). For instance, the strategy for anorder to trade one percent of a stock's average daily volume (ADV) isdifferent from the strategy for an order to trade 50 percent of ADV. Anorder size dimension 204 is represented along a y-axis of the cube 200,ranging from small orders to large orders.

The order size dimension 204 can be quantified by one or morecomponents. One example size component is the number of shares, whichmay be expressed in raw numbers of shares being traded or as apercentage of some metric (e.g., percentage of outstanding shares,percentage of ADV, etc.). Another component that might affect order sizeis market capitalization, as an order to trade a particular number ofshares is often easier to accommodate for large-cap stocks than forsmall-cap stocks. Other components that might affect order size includesector type and market (e.g., NYSE, NASDAQ, OTC, etc.).

A third dimension of order difficulty in cube 200 is trade urgency. Theexecution strategy for a trade with a two-day horizon differs, forexample, from the strategy for a trade with a two-hour horizon. A tradeurgency dimension 206 is represented along a z-axis of the cube 200,ranging from low trade urgency to high trade urgency.

The trade urgency dimension may be measured in a number of ways. One wayto quantify trade urgency is trading alpha, which is a measure of thelikely price change over the trader's execution horizon, aside from theliquidity impact of the execution itself. Trading alpha is distinguishedfrom a portfolio manager's (PM) alpha, which is a measure of what abuy-side manager expects to make over a longer term horizon. Thus, whilethe portfolio manager's strategic investment horizon may be months oryears, a trader's tactical execution horizon ranges from a few hours toa few days. Trading alpha depends on the alpha of the underlyinginvestment strategy, as well as the trading of other marketparticipants. Passive investment strategies, for example, with nolong-term PM alpha, may still have a positive trading alpha. Forinstance, passive funds, such as index funds, have little or no PMalpha, but still may exhibit high trading alphas at times. When the S&P500 is periodically updated to allocate the correct company weightingsand potentially add/remove companies, funds that track this index wantto readjust rapidly the stocks in their portfolios. This results in ahigh trading alpha for a period of time.

Another component affecting the trade urgency dimension 206 isvolatility. Stock volatility is measured in terms of the number ofshares traded each day as compared to historical averages, such as ADV.Stocks exhibiting high volatility tend to have higher trade urgency thanstocks with low volatility.

Execution risk is another factor influencing trade urgency. Executionrisk results from random price changes where the price is equally likelyto move up or down, and the longer a trader takes to execute an orderthe more the execution risk. Over many executions these random pricechanges average to zero. Because of the correlation between time toexecution and execution risk, however, traders that dislike risk havehigher urgency to trade, especially in volatile stocks.

Beta might be considered yet another component of the trade urgencydimension 206. Beta is the measure of individual stock price movementrelative to the overall movement of the market. Stocks with a beta ofone (β=1) move inline with the market. A beta of more than one (β>1)indicates that the stock generally experiences higher price movement incomparison to the market, and a beta of less than one (β<1) indicatesthat the stock generally experiences less price movement in comparisonto the market. Thus, stocks with a beta greater than one (β>1) tend tohave higher trade urgency than stocks with beta of less than one.

Using the cube 200, trade orders may be classified according to thethree dimensions of order difficulty. The cube 200 may define acontinuous scale along which to classify trade orders, or alternativelydefine discrete categories having predefined thresholds that are used tocharacterize the various trade orders. In the illustratedimplementation, each dimension is divided into three categories (i.e.,there are two classification thresholds for each dimension) resulting in27 distinct blocks. Thus, the liquidity dimension 202 defines threecategories including more liquidity, average liquidity, and lessliquidity; the order size dimension 204 defines three categoriesincluding small orders, medium-sized orders, and large orders; and thetrade urgency dimension 206 defines three categories including lowurgency, medium urgency, and high urgency. It is noted that, in otherimplementations, the cube 200 may define more or less than 27 blocks.Further, as noted above, the cube may not define any discrete blocks atall, but simply rely on a continuously variable scale.

For ease of continuing discussion, suppose that the model is implementedto handle trade orders that resolve into one of the eight corner blocksof the cube 200. These corner clocks represent extremes of the variousdimensions. This simplification ignores intermediate orders (e.g.,mid-size in mid-cap stocks) as these orders can be difficult to finesseinto the right execution strategy and the cost of not using exactly theright strategy is small in comparison to the extreme orders. Withrespect to the eight corner blocks, the easiest orders in the cube 200are the low-urgency, small orders in highly liquid stocks, asrepresented by a corner block 210. In contrast, the most difficultorders are the high-urgency, large orders in less liquid stocks, asrepresented by a corner block 212.

To position trade orders in the cube 200, the trade orders arequantified along the three dimensions according to one or morecomponents influencing each of the dimensions. Thresholds are set forthe components to define the classification categories. Consider oneexample set of classification thresholds. To quantify the liquiditydimension 202, a stock's market capitalization is used to classifyorders such that orders in stocks with a market capitalization greaterthan $10 billion are deemed to have high liquidity and orders in stockswith a market capitalization of less than $1 billion are deemed to havelow liquidity. To quantify the order size dimension 204, one possibleclassification of orders is to deem orders of less than 0.25 percent ofADV as small orders and orders of more than 15 percent of ADV as largeorders.

To quantify the trade urgency dimension 206, trading alpha ranges fordifferent investment strategies can be ascertained using data from pastexecutions and input from portfolio managers. As one example, tradingalpha ranges for a predetermined time horizon can be defined, such as atrading alpha range from 0 to 80 basis points (bps) over a five-dayhorizon. Traders with low trading alphas have low urgency to trade,while traders with high trading alphas have high urgency to trade. Onepossible way to classify orders along the trade urgency dimension 206 isto construe orders with a trading alpha of less than 10 bps as lowurgency and orders with a trading alpha of more than 50 bps as highurgency.

Table 1 summarizes the example set of classification thresholds for thecube 200.

TABLE 1 Example Set Classification Thresholds for Cube 200 MODELDIMENSION CLASSIFICATION THRESHOLD Liquidity Dimension Low liquidity <$1billion capitalization High liquidity: >$10 billion capitalization OrderSize Dimension Small Order size: <0.25% ADV Large Order size: >15% ADVTrade Urgency Low Urgency: <10 bps High urgency: >50 bps

Given this example set of classifications, the easiest orders to handle(i.e., corner block 210) are in stocks with a market capitalization ofmore than $10 billion, for an order size of less than 0.25 percent ofADV, and with a trading alpha of less than 10 bps. In contrast, the mostdifficult orders to accommodate (i.e., corner block 212) are in stockswith a market capitalization of less than $1 billion, for an order sizeof more than 15 percent of ADV, and with a trading alpha of more than 50bps.

Mapping Model to Execution Strategies

Now that a set of classification values is established for thedimensions, the various discrete blocks of the cube 200 are mapped toexecution strategies. This converts the cube 200 into a decision makingtool, in which trade orders are fitted to locations in the cube 200 andexecution strategies mapped to these locations are selected forexecuting the trade orders.

FIG. 3 shows one possible mapping of trade orders into executionstrategies using the cube model 200. The various blocks of the cube 200represent varying degrees of order difficulty. For ease of discussion,FIG. 3 illustrates the eight corner blocks arranged vertically accordingto order difficulty, with the top block 210 representing the easiesttrade order to execute and the bottom block 212 representing the mostdifficult trade order to execute. Between these two blocks are sixintermediate blocks 302-312 ranging from easier to more difficult.

Each category of trade order represented by the block is mapped to anassociated execution strategy. Example execution strategies 320 arelisted in conjunction with each block of the cube 200. For instance, forlow urgency, small orders of more liquid stock (e.g., large-capequities) (block 210), possible strategies include (1) DMA small-orderalgorithms, or (2) DMA limit orders to smart routers, ECNs, or brokerplatforms. For low urgency, large orders for more liquid stocks (block304), possible strategies include (1) VWAP (volume-weighted averageprice) algorithms and (2) DMA crossing networks, and (3) in-housesegmentation of the order into multiple smaller orders and execution ofthese smaller orders over time. For high-urgency, small orders in moreliquid stocks (block 308), a possible strategy is DMA market orders tosmart-routers, ECNs, or broker platforms.

Moving from easy to difficult orders, there are fewer choices andbroker-dealer capital becomes more important. For high-urgency, largeorders for more liquid stocks (block 310), possible execution strategiesinclude (1) shortfall algorithms, (2) broker-dealer capital, and (3)broker work order aggressively. For high-urgency, small or large ordersin less liquid stocks (bottom two blocks 312 and 212), the option issimply broker-dealer capital.

Deciding what strategies to associate with the various regions of thecube 200 can be based on many factors. One factor is transaction cost.The tool designer may wish to map types of trade orders represented inthe cube 200 to execution strategies that result in the lowesttransaction costs. Another factor may be timing. In this case, the tooldesigner may wish to map types of trade orders represented in the cube200 to execution strategies that process the trade orders the fastest.Another factor may be the expected spectrum of trade order types and theresources available to process the different types. Thus, the tooldesigner may map the trade types to various strategies that result in aneven distribution across the strategies to avoid any processingbottlenecks.

Additionally, the tool may employ more than one set of mappings anddynamically shift among the mappings to use different strategies. Forexample, the tool may implement a first mapping designed to minimizetransaction costs during the trading day, and then shift to a secondmapping that seeks to process the trades as fast as possible as thetrading day draws to a close.

Strategy Selection Tool

FIG. 4 shows one example implementation of the strategy selection tool110, where the tool is configured as software executing on one or moreservers 400, such as those that support the trading system 108 (FIG. 1).The server(s) 400 is equipped with one or more processing units 402 andmemory 404 (e.g., volatile, non-volatile, and persistent memory). Anetwork interface 406 is also provided to facilitate access to andcommunication over a network, such as network 106 (FIG. 1).

The strategy selection tool 110 is shown stored in memory 404 andexecutes on the processing unit(s) 402 to evaluate trade orders and mapthem to preferred execution strategies. The strategy selection tool 110may include a number of components or modules. In this implementation,the strategy selection tool 110 has an order handler 410, an executionstrategy selector 412, a library of execution strategies 114, and a userinterface (UI) module 414.

The order handler 410 receives trade orders from the sellers. The orderscan be processed in a queue (e.g., first in first out, etc.) orprioritized based on time horizons to complete the trade or othersuitable metrics. The order handler 410 may also parse the trade orderto identify the parameters, such as action (order to sell), equity name,number of shares, price, and so forth.

The execution strategy selector 412 ascertains the constituent valuesfor certain parameters in the trade orders. The constituent values ofinterest pertain to the dimensions established by the multi-dimensionalmodel 112. Continuing the above example of the cube 200 for equitytrading, the selector 412 determines constituent values related to theliquidity, order size, and trade urgency of the trade order. Theexecution strategy selector 412 then compares these constituent valuesto a predetermined classification set established for the model 112. Oneexample classification set is provided above in Table 1. Through thiscomparison, the trade orders are categorized within the cube 200. Foreach trade order, the execution strategy selector 412 selects anappropriate execution strategy from the library 114, where the selectedstrategy corresponds to the category of trade orders in the cube.

When the appropriate execution strategy is identified, the UI module 414presents the execution strategy via graphical user interface (GUI)displayed on a trader screen, such as screen 420. The UI module 414provides a layout that lists, for example, the trade order, itsdimensions, the priority execution strategy identified by the strategyselection tool 110, and possible secondary strategies. In the example ofFIG. 4, the trader screen 420 depicts a trade order number, the tradefacts (e.g., 10,000 SH of ABC Corp.), and the dimension constituentvalues that form the criteria of the decision making tool (small-cap,20% of ADV, 65 bps). The screen 420 also provides the preferredexecution strategy for this set of values, which is broker-dealercapital.

Accordingly, for each trade order, the strategy selection tool 110automatically identifies the trade execution strategies and presentsthem in a timely manner. The trader can then make quicker, more informedand cost-effective decisions when handling a large volume of trades.

Operation

FIGS. 5 and 6 illustrate computerized processes for model-basedselection of trade execution strategies. Each of the processes isillustrated as a collection of blocks in a logical flow graph, whichrepresent a sequence of operations that can be implemented, in whole orin part, in hardware, software, or a combination thereof. In the contextof software, the blocks represent computer-executable instructions that,when executed by one or more processors, perform the recited operations.Generally, computer-executable instructions include routines, programs,objects, components, data structures, and the like that performparticular functions or implement particular abstract data types. Thesequence in which the operations are described is not intended to beconstrued as a limitation, and any number of the described blocks can becombined and/or rearranged in other sequences to implement the process.

For discussion purposes, the processes are described with reference tothe architecture, models, and system of FIGS. 1-4. It is noted, however,that the processes may be implemented in other architectures andsystems, and employ other types of multi-dimensional models.

FIG. 5 shows a computerized process 500 for establishing and using amulti-dimensional model for selecting trade execution strategies. At502, a multi-dimensional model having at least three dimensions isdefined. Each dimension pertains to an attribute of a trade thatreflects order difficulty. In the example described above for tradingequities, the cube 200 defines three dimensions of order difficulty:liquidity, order size, and trade urgency. In other implementations, themodel may include additional dimensions, or define different ones. Forexample, other dimensions for trading equities might include the marketenvironment and complimentary strategies (e.g., is the trade part of anarbitrage strategy). Multi-dimensional models for trading other types offinancial instruments might further employ entirely different tradingdimensions.

At 504, one or more quantifiable components for each dimension in themodel are established, along with a set of classification thresholdsused to determine where the trade order fits in the model. As anexample, for the order size dimension, trade orders may be quantifiedaccording to percentage of average daily volume (ADV). Quantifying tradeorders for the trade urgency dimension may be accomplished by measuringtrading alpha and/or volatility. A measure of market capitalization maybe used to quantify trade orders for the liquidity dimension.

Once the dimensions are quantified, classification thresholds are set toprovide discrete decision making points in the multi-dimensional model.One example set of classification thresholds for the cube 200 isprovided above in Table 1. This classification set defines threedistinct categories along each dimension, thereby establishing 27discrete blocks throughout the cube 200. Trade orders can then beresolved to one of 27 discrete blocks depending upon the constituentvalues along the dimensions and how these values compare to theclassification thresholds. For instance, trade orders for stocks with amarket capitalization of more than $10 billion, having an order size ofless than 0.25 percent of ADV and a trading alpha of less than 10 bps,resolve to the corner block 210 of cube 200. In contrast, trade ordersfor stocks with a market capitalization of less than $1 billion, havingan order size of more than 15 percent of ADV and a trading alpha of morethan 50 bps, resolve to a different block 212 of the cube 200.

At 506, the multi-dimensional model is mapped into execution strategieswith associated priorities. Any number of execution strategies may beprovided, and that number need not equate to the number of discreteblocks of the model. Moreover, trade orders that resolve to differentblocks in the model may be mapped to the same execution strategy. FIG. 3shows on example mapping, where eight corner blocks of the cube 200 aremapped into various execution strategies.

At 508, trade orders are processed using the model-based selection ofthe execution strategies. Trade orders are received, parsed into theirconstituent values, and applied against the classification set toresolve into a block of the cube. Once fitted to a block, the one ormore execution strategies to which the block is mapped are selected andpresented to the trader. One particular implementation of this operationis described below in more detail with reference to FIG. 6.

As the multi-dimensional model is used, its effectiveness at makingtimely and cost-effective decisions can be monitored. Accordingly, anoptional set of operations can be performed (as represented by thedashed arrow continuing from block 508) to establish a control loop.

At 510, the effectiveness of the model for handling trade orders andselecting appropriate execution strategies is evaluated. Effectivenessmay be measured, and hence evaluated, in any number of ways. Ifminimizing transaction costs is the objective, for example, themonitoring may involve ascertaining the costs associated with each tradeexecution and then evaluating whether the model chose the mostcost-effective strategy for the given trade order. If reducing decisionmaking time is a goal, the monitoring may involve detecting whether atrader's ability to handle more trades improves or worsens.

At 512, based on the evaluation, modifications may be made to the model(i.e., dimensions, components), or to the classification sets used toquantify the model dimensions, and/or to the model-to-strategy mapping.After modifications are made, the model is employed to handle futuretrade orders. The effectiveness may once again be measured, and furthermodifications made. This control cycle may be repeated multiple times tofurther refine the operation, or as management goals change.

FIG. 6 shows one example implementation of operation 508 (FIG. 5) forprocessing trade orders using the multi-dimensional model. The operation508 is illustrated as having some actions performed at the buy-sidetrading system (e.g., system 108) and some actions being performed at atrade execution entity, such as ECNs 116 or broker platform 118 inFIG. 1. The actions performed at the buy-side trading system may beimplemented, for example, by the strategy selection tool 110 executingon the servers of the trading system 108.

At 602, a trade order is received. As one example, the trade order isrouted from a seller over a network and received at the trading system108. The trade order is passed to the order handler module 410 whichparses the order into various elements, such as equity name, number ofshares, and so forth.

At 604, the constituent values for the trade order are assessed.Depending on the number of dimensions in the model, there may be threeor more constituent values to assess. Thus, this operation may becomposed as multiple sub-processes. In the case of the cube 200,assessing constituent values involves determining liquidity of theunderlying equity at 604(1), determining the size of the trade order at604(2), and determining trade urgency at 604(3). Act 604 may beperformed, for example, by the strategy selection tool 110, andparticularly, the execution strategy selector 412. In oneimplementation, the execution strategy selector 412 knows how toquantify each dimension and extracts the appropriate values from thetrade order that pertain to that dimension. For determining liquidity,for example, the selector 412 understands that this dimension isquantified according to market capitalization, and hence ascertains fromthe trade order the market capitalization of the equity being traded.

At 606, the constituent values are compared to the classification setfor the multiple dimensions to place the trade orders within the model.In the example classification set in Table 1 for cube 200, trade ordersare resolved to one of the 27 discrete blocks depending upon how theconstituent values compare to the classification set. For instance,trade orders for stocks with a market capitalization of more than $10billion, having an order size of less than 0.25 percent of ADV and atrading alpha of less than 10 bps, resolve to one block 210 of cube 200.In contrast, trade orders for stocks with a market capitalization ofless than $1 billion, having an order size of more than 15 percent ofADV and a trading alpha of more than 50 bps, resolve to a differentblock 212 of the cube 200.

At 608, the execution strategy for the trade order is selected accordingto the model-to-strategy mapping. Thus, if the trade order resolves toone location in the model (e.g., category block 210 of cube 200), theexecution strategy in the library 114 to which that location in themodel is mapped is selected as being the preferred strategy. One examplemodel-to-strategy mapping for the cube 200 is provided in FIG. 3.Accordingly, for a trade order resolving to the block 210 in cube 200(i.e., more liquidity, small orders, low urgency), the executionstrategy selector 412 selects one of the possible strategies thatinclude (1) DMA small-order algorithms, or (2) DMA limit orders to smartrouters, ECNs, or broker platforms. Many other mappings are possibledepending upon the management goals.

At 610, the selected execution strategies are presented to the trader.The execution strategies may be presented, for example, via a graphicalUI depicted on the trader's computer screen, such as that illustrated inFIG. 4. This allows the trader to consider the offered selections, andrender a final decision on how the trade order will ultimately beexecuted.

At 612, the trade order is routed for execution according to thestrategy chosen by the trader. The trade order may be routed, forexample, to ECNs, brokers, a trading algorithm, or any other mechanismto execute the trade.

In the event that the trade is routed to another entity, at 614, thetrade order is received at the trading entity for execution according tothe strategy selected using a multi-dimensional model. At 616, the tradeorder is executed by the trading entity according to the strategy.

CONCLUSION

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described. Rather,the specific features and acts are disclosed as exemplary forms ofimplementing the claims.

1.-8. (canceled)
 9. A computer-implemented method, comprising:classifying trade orders according to a multi-dimensional model havingat least three dimensions, each dimension pertaining to order difficultyof executing a trade order; and using the multi-dimensional model toselect one or more execution strategies for executing the trade orders.10. A computer-implemented method as recited in claim 9, wherein thedimensions comprise order size, liquidity, and trade urgency.
 11. Acomputer-implemented method as recited in claim 9, wherein the tradeorders pertain to trading equities and the dimensions compriseliquidity, order size, and trade urgency, said classifying comprising:determining the liquidity of the equities identified in the trade ordersaccording to at least one of (1) market capitalization, (2) marketexchange, or (3) timing; determining the order size of the trade ordersaccording to at least one of (1) a number of shares, (2) a function ofvolume, (3) market capitalization, or (4) market exchange; anddetermining the trade urgency of the trade orders according to at leastone of (1) trading alpha, (2) execution risk, or (3) volatility.
 12. Acomputer-implemented method as recited in claim 9, further comprisingpresenting the one or more execution strategies thus selected forconsideration by a trader.
 13. One or more computer-readable mediastoring computer-executable instructions that, when executed on one ormore processing units, perform the computer-implemented method recitedin claims
 9. 14.-23. (canceled)
 24. One or more computer-readable mediastoring computer-executable instructions that, when executed on one ormore processors, performs acts comprising: receiving multiple tradeorders; categorizing the trade orders according to at least threedifferent measures of order difficulty for executing the trade orders,wherein one of the measures of order difficulty is trade urgency;selecting execution strategies for the trade orders based on the tradeorders' associated measures of order difficulty; and presenting theexecution strategies for consideration by a trader.
 25. One or morecomputer-readable media as recited in claim 24, wherein the categorizingcomprises: assessing constituent values of the trade orders for eachdimension in a multi-dimensional model having at least three dimensions,said each dimension pertaining to an order difficulty in executing thetrade orders such that the multi-dimensional model characterizes tradeorders in terms of order difficulty; and comparing the constituentvalues to a classification set to resolve the trade orders to differentcharacterizations of the multi-dimensional model.
 26. A computing systemcomprising: at least one processing unit; and the one or morecomputer-readable media as recited in claim 24, the computer-executableinstructions being executable on the processing unit.
 27. A systemcomprising: memory; at least one processing unit coupled to access thememory; and a tool stored in the memory and executable on the processingunit, the tool classifying trade orders according to a multi-dimensionalmodel having at least three dimensions where each dimension pertains toorder difficulty of executing the trade order, the tool automaticallyselecting execution strategies for the trade orders based on theclassification of the trade orders respective to the three dimensions.28. A system as recited in claim 27, wherein the three dimensionscomprise liquidity, order size, and trade urgency.
 29. A system asrecited in claim 27, wherein the tool comprises a user interface topresent the selected execution strategies for consideration by a trader.30.-33. (canceled)